cd '/home/dpirvu/project/paper_prefactor/'
/home/dpirvu/project/paper_prefactor
import os,sys
sys.path.append('/home/dpirvu/python_stuff/')
sys.path.append('/home/dpirvu/project/paper_prefactor/bubble_codes/')
sys.path.remove('/home/dpirvu/DarkPhotonxunWISE/hmvec-master')
print(sys.path)
from plotting import *
from bubble_tools import *
from experiment import *
from celluloid import Camera
from matplotlib.colors import LogNorm
%matplotlib inline
['/home/dpirvu/project/paper_prefactor', '/cm/shared/apps/python/python37/lib/python37.zip', '/cm/shared/apps/python/python37/lib/python3.7', '/cm/shared/apps/python/python37/lib/python3.7/lib-dynload', '', '/home/dpirvu/.local/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages/IPython/extensions', '/home/dpirvu/.ipython', '/home/dpirvu/python_stuff/', '/home/dpirvu/project/paper_prefactor/bubble_codes/'] ['/home/dpirvu/project/paper_prefactor', '/cm/shared/apps/python/python37/lib/python37.zip', '/cm/shared/apps/python/python37/lib/python3.7', '/cm/shared/apps/python/python37/lib/python3.7/lib-dynload', '', '/home/dpirvu/.local/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages', '/cm/shared/apps/python/python37/lib/python3.7/site-packages/IPython/extensions', '/home/dpirvu/.ipython', '/home/dpirvu/python_stuff/', '/home/dpirvu/project/paper_prefactor/bubble_codes/']
case = 'minus'
general = get_general_model(case)
tempList, massq, right_Vmax, V, dV, Vinv, nTimeMAX, minSim, maxSim = general
tmp = 2
maxSim = (1000 if tmp == 0 else 2000)
temp, m2, sigmafld = get_model(*general, tmp)
exp_params = [nLat, m2, temp]
print('Experiment', exp_params)
Experiment [2048, 0.7, 0.2]
print('Looking at at T, m2, sigma:', temp, m2, sigmafld)
crit_radList = np.array(np.linspace(5, 50, 10), dtype='int'); print(crit_radList)
crit_threshList = right_Vmax + np.linspace(0.5, 5, 10) * sigmafld
crit_threshList = np.array([round(ii, 3) for ii in crit_threshList]); print(crit_threshList)
win = 250
plots = False
get_data = True
get_averaged = True
if get_data:
path = decay_times_file(*exp_params, minSim, maxSim, nTimeMAX)
if os.path.exists(path):
decay_times = np.load(path)
minDecTime = 256
alltimes = decay_times[:,1]
simList2Do = decay_times[alltimes>=minDecTime, 0]
all_data = []
for sim in range(minSim, maxSim):
path2RESTsim = rest_sim_location(*exp_params, sim)
if os.path.exists(path2RESTsim):
sim, bubble, totbeta = np.load(path2RESTsim, allow_pickle=True)
# real = np.copy(bubble)
# real = np.abs(real[0])
# real = gaussian_filter(real, 1, mode='nearest')
# nT, nN = np.shape(real)
# tcen, xcen = find_nucleation_center(real, phieq, 2.7, 30)
# t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)
# test = bubble[0, tcen, xcen-50:xcen+50]
# if np.nanmean(test) < 0:
# bubble = - bubble
all_data.append(np.array([sim, bubble]))
if len(all_data) > 300:
break
print('Total bubbles included:', len(all_data))
if get_averaged:
for cind, cth in enumerate(crit_radList):
for tind, tsh in enumerate(crit_threshList):
path = average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy'
if os.path.exists(path): continue
stacks = stack_bubbles(all_data, win, phieq, tsh, cth, plots)
avstack = average_stacks(stacks, win, normal, plots=False)
#except:
# print('Skipped cind, tind', cind, tind)
# continue
np.save(path, avstack)
print('Done cind, tind', cind, tind)
Looking at at T, m2, sigma: 0.2 0.7 0.3415271460137985 [ 5 10 15 20 25 30 35 40 45 50] [1.171 1.342 1.513 1.683 1.854 2.025 2.196 2.366 2.537 2.708]
/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:38: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
Total bubbles included: 301 142 simulations for this combination.
/home/dpirvu/project/paper_prefactor/bubble_codes/bubble_tools.py:556: RuntimeWarning: Mean of empty slice mean = np.nanmean(whole_bubble, axis=0) /home/dpirvu/.local/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice. keepdims=keepdims)
Done cind, tind 0 0 238 simulations for this combination. Done cind, tind 0 2 255 simulations for this combination. Done cind, tind 0 3 261 simulations for this combination. Done cind, tind 0 4 268 simulations for this combination. Done cind, tind 0 5 270 simulations for this combination. Done cind, tind 0 6 272 simulations for this combination. Done cind, tind 0 7 266 simulations for this combination. Done cind, tind 0 8 183 simulations for this combination. Done cind, tind 1 0 236 simulations for this combination. Done cind, tind 1 1 252 simulations for this combination. Done cind, tind 1 2 259 simulations for this combination. Done cind, tind 1 3 266 simulations for this combination. Done cind, tind 1 4 269 simulations for this combination. Done cind, tind 1 5 272 simulations for this combination. Done cind, tind 1 6 274 simulations for this combination. Done cind, tind 1 7 277 simulations for this combination. Done cind, tind 1 8 277 simulations for this combination. Done cind, tind 1 9 221 simulations for this combination. Done cind, tind 2 0 250 simulations for this combination. Done cind, tind 2 1 257 simulations for this combination. Done cind, tind 2 2 266 simulations for this combination. Done cind, tind 2 3 269 simulations for this combination. Done cind, tind 2 4 272 simulations for this combination. Done cind, tind 2 5 275 simulations for this combination. Done cind, tind 2 6 277 simulations for this combination. Done cind, tind 2 7 279 simulations for this combination. Done cind, tind 2 8 279 simulations for this combination. Done cind, tind 2 9 255 simulations for this combination. Done cind, tind 3 1 264 simulations for this combination. Done cind, tind 3 2 266 simulations for this combination. Done cind, tind 3 3 271 simulations for this combination. Done cind, tind 3 4 274 simulations for this combination. Done cind, tind 3 5 277 simulations for this combination. Done cind, tind 3 6 279 simulations for this combination. Done cind, tind 3 8 281 simulations for this combination. Done cind, tind 3 9 250 simulations for this combination. Done cind, tind 4 0 257 simulations for this combination. Done cind, tind 4 1 266 simulations for this combination. Done cind, tind 4 2 271 simulations for this combination. Done cind, tind 4 3 275 simulations for this combination. Done cind, tind 4 4 277 simulations for this combination. Done cind, tind 4 5 278 simulations for this combination. Done cind, tind 4 6 279 simulations for this combination. Done cind, tind 4 7 281 simulations for this combination. Done cind, tind 4 8 281 simulations for this combination. Done cind, tind 4 9 251 simulations for this combination. Done cind, tind 5 0 271 simulations for this combination. Done cind, tind 5 2 275 simulations for this combination. Done cind, tind 5 3 277 simulations for this combination. Done cind, tind 5 4 279 simulations for this combination. Done cind, tind 5 5 280 simulations for this combination. Done cind, tind 5 6 280 simulations for this combination. Done cind, tind 5 7 282 simulations for this combination. Done cind, tind 5 8 256 simulations for this combination. Done cind, tind 6 0 269 simulations for this combination. Done cind, tind 6 1 274 simulations for this combination. Done cind, tind 6 2 277 simulations for this combination. Done cind, tind 6 3 280 simulations for this combination. Done cind, tind 6 4 281 simulations for this combination. Done cind, tind 6 5 281 simulations for this combination. Done cind, tind 6 6 284 simulations for this combination. Done cind, tind 6 7 285 simulations for this combination. Done cind, tind 6 8 286 simulations for this combination. Done cind, tind 6 9 261 simulations for this combination. Done cind, tind 7 0 273 simulations for this combination. Done cind, tind 7 1 275 simulations for this combination. Done cind, tind 7 2 280 simulations for this combination. Done cind, tind 7 3 281 simulations for this combination. Done cind, tind 7 4 285 simulations for this combination. Done cind, tind 7 5 285 simulations for this combination. Done cind, tind 7 6 287 simulations for this combination. Done cind, tind 7 7 287 simulations for this combination. Done cind, tind 7 8 287 simulations for this combination. Done cind, tind 7 9 266 simulations for this combination. Done cind, tind 8 0 277 simulations for this combination. Done cind, tind 8 1 280 simulations for this combination. Done cind, tind 8 2 283 simulations for this combination. Done cind, tind 8 3 285 simulations for this combination. Done cind, tind 8 4 286 simulations for this combination. Done cind, tind 8 5 287 simulations for this combination. Done cind, tind 8 6 287 simulations for this combination. Done cind, tind 8 7 287 simulations for this combination. Done cind, tind 8 8 287 simulations for this combination. Done cind, tind 8 9 275 simulations for this combination. Done cind, tind 9 0 282 simulations for this combination. Done cind, tind 9 1 283 simulations for this combination. Done cind, tind 9 2 287 simulations for this combination. Done cind, tind 9 3 287 simulations for this combination. Done cind, tind 9 4 287 simulations for this combination. Done cind, tind 9 5 287 simulations for this combination. Done cind, tind 9 6 287 simulations for this combination. Done cind, tind 9 7 288 simulations for this combination. Done cind, tind 9 8 288 simulations for this combination. Done cind, tind 9 9
print('Looking at at T, m2, sigma:', temp, m2, sigmafld)
get_plotcomp = True
delt1 = 100
if get_plotcomp:
varmat = np.zeros((len(crit_radList), len(crit_threshList)))
for cind, cth in enumerate(crit_radList):
for tind, tsh in enumerate(crit_threshList):
bubble = np.load(average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy')
bubble[bubble==0.] = 'nan'
bubble2measure = np.abs(bubble[0,0])
nT, nN = np.shape(bubble2measure)
# for crit_rad in crit_radList:
# for crit_thresh in crit_threshList:
# tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
# tcen -= 35
# tl,tr = max(0, tcen-delt1), min(nT, tcen+delt1+1)
# xl,xr = max(0, xcen-delt1), min(nN, xcen+delt1+1)
# err_f = bubble[1, 0][tl:tr,xl:xr] #field variance
# err_m = bubble[1, 1][tl:tr,xl:xr] #momentum variance
# varmat[cind, tind] += np.nanmean(np.abs(err_f))
tcen, xcen = find_nucleation_center(bubble2measure, phieq, tsh, cth)
tcen -= 35
tl,tr = max(0, tcen-delt1), min(nT, tcen+delt1+1)
xl,xr = max(0, xcen-delt1), min(nN, xcen+delt1+1)
err_f = bubble[1, 0][tl:tr,xl:xr] #field variance
err_m = bubble[1, 1][tl:tr,xl:xr] #momentum variance
varmat[cind, tind] += np.nanmean(np.abs(err_f))
colmin, rowmin = np.where(varmat == np.nanmin(varmat))
final_crit_rad = crit_radList[colmin][0]
final_crit_thresh = crit_threshList[rowmin][0]
fig, ax = plt.subplots(1,1, figsize = (3.5,3))
ext = [crit_threshList[0], crit_threshList[-1], crit_radList[0], crit_radList[-1]]
im0 = plt.imshow(varmat, interpolation=None, norm=LogNorm(), extent=ext, aspect='auto', origin='lower', cmap='RdBu')
clb = plt.colorbar(im0)
clb.ax.set_title(r'${\rm var} \, \left< \bar{\varphi} \right> $', size=11, horizontalalignment='center', verticalalignment='bottom')
clb.ax.yaxis.set_offset_position('right')
plt.plot(final_crit_thresh, final_crit_rad, color='white', marker='*')
#plt.plot(final_crit_thresh + 0.5*(crit_threshList[1]-crit_threshList[0]), final_crit_rad + 0.5*(crit_radList[1]-crit_radList[0]), color='white', marker='*')
xx, yy = np.meshgrid(crit_threshList, crit_radList)
ax.contour(xx,yy,np.abs(np.log(varmat)), levels=6, aspect='auto', interpolation='gaussian', extent=ext, origin='lower', colors='k', linewidths=0.5)
ax.set_ylabel(r'$R$')
ax.set_xlabel(r'$\bar{\phi}$')
beautify(ax, times=-90)
ax.legend(title=r'$T = {:.2f}$'.format(temp), labelspacing=0., frameon='true', facecolor='white', framealpha=0.85, edgecolor='k', borderpad=0.3)
fig.tight_layout()
plt.savefig('./plots/residual_averaging_T'+str(temp)+'.pdf')
plt.show()
Looking at at T, m2, sigma: 0.2 0.7 0.3415271460137985
/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:49: UserWarning: The following kwargs were not used by contour: 'aspect', 'interpolation' No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
if True:
tp = 0 # 0 for average, 1 for error
cp = 0 # 0 - field, 1 - momentum
delt = 100; print(delt)
fig, ax = plt.subplots(len(crit_radList), len(crit_threshList), figsize = (len(crit_threshList)*5, len(crit_radList)*5), sharey=True, sharex=True)
for cind, cth in enumerate(crit_radList):
for tind, tsh in enumerate(crit_threshList):
bubble = np.load(average_file(*exp_params)+'_critrad'+str(cth)+'_crittsh'+str(tsh)+'.npy')
bubble[bubble==0.] = 'nan'
bubble2measure = np.abs(bubble[tp, cp])
if cp==0:
bubble2measure[bubble2measure > tsh] = tsh
try:
tcen, xcen = find_nucleation_center(bubble2measure, phieq, tsh, cth)
except:
continue
tcen-=35
nT, nN = np.shape(bubble2measure)
tl,tr = max(0, tcen-delt), min(nT, tcen+delt+1)
xl,xr = max(0, xcen-delt), min(nN, xcen+delt+1)
ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])
bubble2plot = bubble2measure[tl:tr,xl:xr]
im0 = ax[cind,tind].imshow(bubble2plot, extent=ext, interpolation=None, origin='lower', cmap='tab20c')
clb0 = plt.colorbar(im0, ax = ax[cind,tind], shrink=0.6)
lab = r'${{\rm size}}={:.0f}'.format(cth)+r'$, {{\rm thresh}}={:.2f}'.format(tsh)
ax[cind,tind].plot(0,0, ls=None, marker='o', color='k', label=lab)
beautify(ax[cind,tind])
ax[cind,tind].legend(loc=4, fancybox=True, frameon=True, framealpha=0.75, borderpad=0.3)
if tsh == final_crit_thresh and cth == final_crit_rad:
ax[cind,tind].plot(0., 0., color='red', ms=10, marker='*')
plt.tight_layout()
plt.show()
100
rfin = np.argwhere(crit_radList == final_crit_rad)[0,0]
tfin = np.argwhere(crit_threshList == final_crit_thresh)[0,0]
crit_rad = final_crit_rad
crit_thresh = final_crit_thresh
print(rfin, tfin, crit_rad, crit_thresh)
get_final_averaged = True
if get_final_averaged:
path = decay_times_file(*exp_params, minSim, maxSim, nTimeMAX)
if os.path.exists(path):
decay_times = np.load(path)
minDecTime = 256
alltimes = decay_times[:,1]
simList2Do = decay_times[alltimes>=minDecTime, 0]
all_data = []
fig, ax = plt.subplots(1,1, figsize = (4.5,2.5))
for sim in simList2Do:
path2RESTsim = rest_sim_location(*exp_params, sim)
if os.path.exists(path2RESTsim):
sim, bubble, totbeta = np.load(path2RESTsim, allow_pickle=True)
real = np.copy(bubble)
real = np.abs(real[0])
real = gaussian_filter(real, 1, mode='nearest')
nT, nN = np.shape(real)
tcen, xcen = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)
# test = bubble[0, tcen, xcen-50:xcen+50]
# if np.nanmean(test) < 0:
# bubble = - bubble
test = bubble[0, tcen, xcen-100:xcen+100]
plt.plot(test, lw=1)
plt.title(sim)
all_data.append(np.array([sim, bubble]))
print('Total bubbles included:', len(all_data))
plt.show()
stacks = stack_bubbles(all_data, win, phieq, crit_thresh, crit_rad, False)
avstack = average_stacks(stacks, win, normal, True)
np.save(average_file(*exp_params), avstack)
2 2 15 1.513
/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:39: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
Total bubbles included: 828
719 simulations for this combination.
/home/dpirvu/project/paper_prefactor/bubble_codes/bubble_tools.py:556: RuntimeWarning: Mean of empty slice mean = np.nanmean(whole_bubble, axis=0) /home/dpirvu/.local/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1671: RuntimeWarning: Degrees of freedom <= 0 for slice. keepdims=keepdims) No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
def plot_zoomin(real, threshold=2., winsize=100, title=None):
real = real[0]
nT, nN = np.shape(real)
t_centre, x_centre = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
tl_stop, tr_stop = int(max(0, t_centre - winsize)), int(min(nT, t_centre + winsize//2))
xl_stop, xr_stop = int(max(0, x_centre - winsize)), int(min(nN, x_centre + winsize))
real = real[tl_stop:tr_stop, xl_stop:xr_stop]
nT, nN = np.shape(real)
tcen, xcen = find_nucleation_center(real, phieq, crit_thresh, crit_rad)
t, x = np.linspace(-tcen, nT-1-tcen, nT), np.linspace(-xcen, nN-1-xcen, nN)
cds = np.abs(real) > threshold
real[cds] = threshold
simple_imshow([real], x, t, title=title, contour=False, ret=False)
return
path = average_file(*exp_params)+'_critrad'+str(crit_rad)+'_crittsh'+str(crit_thresh)+'.npy'
average_bubble_filtered = np.load(path)
plot_zoomin(average_bubble_filtered[0], threshold=20, winsize=60)
average_bubble_loaded = np.load(average_file(*exp_params))
plot_zoomin(average_bubble_loaded[0], threshold=20, winsize=60)
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
titl = [r'$\bar{\phi}$', r'$\bar{\Pi}$']
for avbub in [average_bubble_loaded, average_bubble_filtered]:
bubble2measure = avbub[0][0]
nT, nN = np.shape(bubble2measure)
tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
print(tcen, xcen)
tcen-= 40
size = 130
tl,tr = max(0, tcen-size), min(nT, tcen+size+1)
xl,xr = max(0, xcen-size), min(nN, xcen+size+1)
ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])
fig, ax = plt.subplots(1, 2, figsize = (6, 3), sharey=True)
for cp in range(2):
# 0 - field, 1 - momentum
bubble2plot = np.copy(avbub[0][cp,tl:tr,xl:xr])
im = ax[cp].imshow(bubble2plot, interpolation=None, extent=ext, origin='lower', cmap='tab20b')
cbar = plt.colorbar(im, ax=ax[cp], shrink=0.7)
cbar.ax.set_title(titl[cp])
nT, nN = np.shape(bubble2plot)
tt = np.linspace(tl-tcen, tr-tcen, nT)
xx = np.linspace(xl-xcen, xr-xcen, nN)
ttt1, xxx1 = np.meshgrid(tt, xx)
lavs = [8, 6][cp]
# ax[cp].contour(xxx1, ttt1, bubble2plot.T, levels=lavs, aspect='auto', interpolation=None, extent=ext, origin='lower', colors='k',linewidths=0.5)
ax[cp].set_xlabel(r'$r$')
ax[0].set_ylabel(r'$t$')
beautify(ax, times=-90)
plt.tight_layout()
plt.savefig('./plots/average_bubble'+str(temp)+'.pdf')
plt.show()
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
253 251
No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument. No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
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tp = 0# 0 for average, 1 for error
titl = [r'$\bar{\phi}$', r'$\bar{\Pi}$']
for avbub in [average_bubble_loaded, average_bubble_filtered]:
bubble2measure = avbub[0][0]
nT, nN = np.shape(bubble2measure)
tcen, xcen = find_nucleation_center(bubble2measure, phieq, crit_thresh, crit_rad)
print(tcen, xcen)
size = 100
tl,tr = max(0, tcen-size), min(nT, tcen+size+1)
xl,xr = max(0, xcen-size), min(nN, xcen+size+1)
ext = np.array([xl-xcen,xr-xcen,tl-tcen,tr-tcen])
fld_fld = np.copy(avbub[0][0])
fld_mom = np.copy(avbub[0][1])
fld_fld[fld_fld > crit_thresh] = crit_thresh
fld_fld = gaussian_filter1d(fld_fld, 1, mode='nearest')
fld_mom = gaussian_filter1d(fld_mom, 1, mode='nearest')
fld_fld = fld_fld[tl:tr, xl:xr]
fld_mom = fld_mom[tl:tr, xl:xr]
kinetic = 0.5*fld_mom**2.
gradient = 0.5*np.gradient(fld_fld, dx)[1]**2.
potential = V(fld_fld)
total = kinetic + gradient + potential
picks = [kinetic, gradient, potential, total]
titl = [r'$\rm Kinetic$', r'$\rm Gradient$', r'$\rm Potential$', r'$\rm Total$']
fig, ax = plt.subplots(1, 4, figsize = (13, 3), sharey=True)
for fi, field in enumerate(picks):
im = ax[fi].imshow(field, extent=ext, origin='lower', cmap='tab20c')
cbar = fig.colorbar(im, ax=ax[fi], shrink=0.7)
nT, nN = np.shape(field)
tt = np.linspace(tl-tcen, tr-tcen, nT)
xx = np.linspace(xl-xcen, xr-xcen, nN)
ttt1, xxx1 = np.meshgrid(tt, xx)
ax[fi].contour(xxx1, ttt1, field.T, levels=8, aspect='auto', interpolation='none', extent=ext, origin='lower', colors='k', linewidths=0.5)
ax[fi].set_title(titl[fi])
ax[fi].set_xlabel(r'$r$')
ax[0].set_ylabel(r'$t$')
plt.savefig('./plots/energies.pdf')
plt.show()
253 251
/cm/shared/apps/python/python37/lib/python3.7/site-packages/ipykernel_launcher.py:43: UserWarning: The following kwargs were not used by contour: 'aspect', 'interpolation'
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